from numpy import *
# 如果说决策树是对每一个分类的预测，那么朴素贝叶斯就是对分类的概率进行计算，然后将概率大的分成预测的类别
# 朴素的体现：每个特征同等重要

## 训练集标签：侮辱类和非侮辱类

## 词表到向量的转换
def loadDataSet():
    postingList=[['my', 'dog', 'has', 'flea', 'problems', 'help', 'please'],    #切分的词条
                 ['maybe', 'not', 'take', 'him', 'to', 'dog', 'park', 'stupid'],
                 ['my', 'dalmation', 'is', 'so', 'cute', 'I', 'love', 'him'],
                 ['stop', 'posting', 'stupid', 'worthless', 'garbage'],
                 ['mr', 'licks', 'ate', 'my', 'steak', 'how', 'to', 'stop', 'him'],
                 ['quit', 'buying', 'worthless', 'dog', 'food', 'stupid']]
    classVec = [0,1,0,1,0,1] # 1,0代表每个数组元素是否为侮辱类
    return postingList, classVec


## 创建词汇列表
def createVocabList(dataSet): # 之后就是传入postingList
    vocabSet = set([])
    for document in dataSet:
        vocabSet = vocabSet | set(document) # 创建两个集合的并集
    return list(vocabSet) 

## 将输入的词汇变成词向量(词集模型set-of-words model)
def setOfWords2Vec(vocabList, inputSet):
    returnVec = [0] * len(vocabList)
    for word in inputSet:
        if word in vocabList:
            returnVec[vocabList.index(word)] = 1
        else:
            print('the word: %s is not in my Vocablary!' % word)
    return returnVec

## 使用贝叶斯分类器训练函数
## 主要公式: p(ci| w) = p(w| ci)p(ci) / p(w)
def trainNB0(trainMatrix, trainCategory): # trainCategory是分类标签数组
    numTrainDocs = len(trainMatrix) # 总共的样本数
    numWords = len(trainMatrix[0]) # 每一组词的个数
    pAbusive = sum(trainCategory) / float(numTrainDocs)
    p0Num = ones(numWords)
    p1Num = ones(numWords)
    p0Denom = 2.0  # 不属于侮辱性的
    p1Denom = 2.0  # 属于侮辱性的
    for i in range(numTrainDocs):
        if trainCategory[i] == 1:
            p1Num += trainMatrix[i]
            p1Denom += sum(trainMatrix[i])
        else:
            p0Num += trainMatrix[i]
            p0Denom += sum(trainMatrix[i])
    p1Vect = log(p1Num / p1Denom)
    p0Vect = log(p0Num / p0Denom)
    return p0Vect, p1Vect, pAbusive

## 朴素贝叶斯分类函数
def classifyNB(vec2Classify, p0Vect, p1Vect, pClass1):
    ## 输入分类向量乘以
    p1 = sum(vec2Classify * p1Vect) + log(pClass1)
    p0 = sum(vec2Classify * p0Vect) + log(1.0 - pClass1)
    print(p1, p0)
    if p1 > p0:
        return 1
    else:
        return 0

## 使用分类器来对词汇数据进行分类
def testingNB():
    listOposts, listClasses = loadDataSet()  
    myVocabList = createVocabList(listOposts)
    trainMat = []
    for postinDoc in listOposts:
        trainMat.append(setOfWords2Vec(myVocabList, postinDoc))
    p0V, p1V, pAb = trainNB0(array(trainMat), array(listClasses))
    testEntry = ['love', 'my', 'dalmation']
    thisDoc = array(setOfWords2Vec(myVocabList, testEntry))
    print(testEntry, 'classified as: ', classifyNB(thisDoc, p0V, p1V, pAb))
    testEntry = ['stupid', 'garbage']
    thisDoc = array(setOfWords2Vec(myVocabList, testEntry))
    print(testEntry, 'classified as: ', classifyNB(thisDoc, p0V, p1V, pAb))


## 词袋模型的修改(bag-of-words-model)
def bagOfWords2VecMN(vocabList, inputSet):
    returnVec = [0] * len(vocabList)
    for word in inputSet:
        if word in vocabList:
            returnVec[vocabList.index(word)] += 1
    return returnVec

def textParse(bigString):
    import re
    listOfTokens = re.split(r'\w*', bigString)
    return [tok.lower() for tok in listOfTokens if len(tok) > 2]

def spamTest():
    docList = [] 
    classList = []
    fullText = []
    for i in range(1, 26): # 因为有25个数据
        wordList = textParse(open('./email/spam/%d.txt' % i).read())
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(1)
        wordList = textParse(open('./email/ham/%d.txt' % i).read())
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(0)
    vocabList = createVocabList(docList)
    trainingSet = list(range(50))
    testSet = []
    for i in range(10):
        randIndex = int(random.uniform(0, len(trainingSet)))
        testSet.append(trainingSet[randIndex])
        del(trainingSet[randIndex])
    trainMat = []
    trainClasses = []
    for docIndex in trainingSet:
        trainMat.append(setOfWords2Vec(vocabList, docList[docIndex]))
        trainClasses.append(classList[docIndex])
    p0V, p1V, pSpam = trainNB0(array(trainMat), array(trainClasses))
    errorCount = 0
    for docIndex in testSet:
        wordVector = setOfWords2Vec(vocabList, docList[docIndex])
        if classifyNB(array(wordVector), p0V, p1V, pSpam) != classList[docIndex]:
            errorCount += 1
    print('the error rate is: ', float(errorCount) / len(testSet))
